preload.py

import streamlit as st
import pymongo
from pymongo.errors import PyMongoError
import pandas as pd
import pickle
import os

#### Handling the db


@st.cache_resource()
def get_database_client():
#    try:
#        client = pymongo.MongoClient(st.secrets["mongodb_string"])
#    except PyMongoError as e:
#        st.error(
#            "We are sorry, we are unable to connect to our database. Please try again later."
#        )
#        st.stop()
#    return client

    mongo_uri = os.environ.get("mongodb_uri") or st.secrets.get("mongodb_string")
    client = pymongo.MongoClient(mongo_uri, uuidRepresentation='standard')
    return client

mongo_client = get_database_client()
# st.toast("Database connected!")
#### Loading the Mappatura database


@st.cache_resource()
def load_database(_mongo_client):

    try:
        col = _mongo_client["poverta_educativa"]["database_mappatura_1"]

        mappatura_tot = pd.DataFrame(list(col.find()))
        mappatura_tot["_id"] = mappatura_tot["_id"].astype(str)
        mappatura_data = mappatura_tot
        #mappatura_data = mappatura_tot.drop(
        #    [   "_id",
        #        "Livello di analisi",
        #        "Budget",
        #        "Budget completo di cofinanziamento (*se noto)",
        #        "Link 2 ",
        #    ],
        #    axis=1,
        #)
        mappatura_data["Data inizio"] = mappatura_data["Data inizio"].astype(str)
        mappatura_data["Data fine (prevista o effettiva)"] = mappatura_data["Data fine (prevista o effettiva)"].astype(str)
    except PyMongoError as e:
        st.error(
            "We are sorry, we are unable to connect to our database. Please try again later."
        )
        st.stop()

    return mappatura_data


mappatura_data = load_database(mongo_client)
# st.toast("Database loaded!")

##### Loading the color data


@st.cache_resource()
def get_color_data():

    try:
        color_data_regioni = pd.read_json("./data/color_data_regioni.json")
        color_data_province = pd.read_json("./data/color_data_province.json")
        color_data_comuni = pd.read_json("./data/color_data_comuni.json")

    except:
        st.error(
            "We are sorry, we are unable to load colormaps. Please try again later."
        )
        color_data_comuni = pd.DataFrame({"name": [], "color": []})
        color_data_regioni = pd.DataFrame({"reg_name": [], "color": []})
        color_data_province = pd.DataFrame({"prov_name": [], "color": []})

    return color_data_regioni, color_data_province, color_data_comuni


presence_regioni = pickle.load(open("./data/presence_regioni_vector.pkl", "rb"))
presence_province = pickle.load(open("./data/presence_province_vector.pkl", "rb"))
presence_comuni = pickle.load(open("./data/presence_comuni_vector.pkl", "rb"))
all_regions = pickle.load(open("./data/all_regions.pkl", "rb"))
all_provinces = pickle.load(open("./data/all_provinces.pkl", "rb"))
all_munis = pickle.load(open("./data/all_munis.pkl", "rb"))



color_data_regioni, color_data_province, color_data_comuni = get_color_data()
# st.toast("Colormaps loaded.")